Python_(programming_language)

Python (programming language)

Python is a general-purpose, high-level programming language. Its design philosophy emphasizes programmer productivity and code readability. Python's core syntax and semantics are minimalistic, while the standard library is large and comprehensive. Its use of whitespace as block delimiters is unusual among popular programming languages.

Python supports multiple programming paradigms (primarily object oriented, imperative, and functional) and features a fully dynamic type system and automatic memory management, similar to Perl, Ruby, Scheme, and Tcl. Like other dynamic languages, Python is often used as a scripting language.

Python was first released by Guido van Rossum in 1991. The language has an open, community-based development model managed by the non-profit Python Software Foundation, which also maintains the de facto standard definition of the language in CPython, the reference implementation.

History

Python was conceived in the late 1980s by Guido van Rossum at CWI in the Netherlands as a successor to the ABC programming language capable of exception handling and interfacing with the Amoeba operating system. Van Rossum is Python's principal author, and his continuing central role in deciding the direction of Python is reflected in the title given him by the Python community, Benevolent Dictator for Life (BDFL).

First publication

In 1991, van Rossum published the code (labeled version 0.9.0) to alt.sources. Already present at this stage in development were classes with inheritance, exception handling, functions, and the core datatypes of list, dict, str and so on. Also in this initial release was a module system borrowed from Modula-3; van Rossum describes the module as "one of Python's major programming units". Python's exception model also resembles Modula-3's, with the addition of an else clause. In 1994 [news://comp.lang.python comp.lang.python], the primary discussion forum for Python, was formed, marking a milestone in the growth of Python's userbase.

Version 1.0

Python reached version 1.0 in January 1994. The major new features included in this release were the functional programming tools lambda, map, filter and reduce. Van Rossum stated that “Python acquired lambda, reduce(), filter() and map(), courtesy of (I believe) a Lisp hacker who missed them and submitted working patches.”

The last version released while van Rossum was at CWI was Python 1.2. In 1995, van Rossum continued his work on Python at the Corporation for National Research Initiatives (CNRI) in Reston, Virginia from where he released several versions.

By version 1.4, Python had acquired several new features. Notable among these are the Modula-3 inspired keyword arguments (which are also similar to Common Lisp's keyword arguments), and built-in support for complex numbers. Also included is a basic form of data hiding by name mangling, though this is easily bypassed.

During van Rossum's stay at CNRI, he launched the Computer Programming for Everybody (CP4E) initiative, intending to make programming more accessible to more people, with a basic 'literacy' in programming languages, similar to the basic English literacy and mathematics skills required by most employers. Python served a central role in this: because of its focus on clean syntax, it was already suitable, and CP4E's goals bore similarities to its predecessor, ABC. The project was funded by DARPA. , the CP4E project is inactive, and while Python attempts to be easily learnable and not too arcane in its syntax and semantics, reaching out to non-programmers is not an active concern.

BeOpen

In 2000, the Python core development team moved to BeOpen.com to form the BeOpen PythonLabs team. CNRI requested that a version 1.6 be released, summarizing Python's development up to the point at which the development team left CNRI. Consequently, the release schedules for 1.6 and 2.0 had a significant amount of overlap. Python 2.0 was the first and only release from BeOpen.com. After Python 2.0 was released by BeOpen.com, Guido van Rossum and the other PythonLabs developers joined Digital Creations.

The Python 1.6 release included a new CNRI license that was substantially longer than the CWI license that had been used for earlier releases. The new license included a clause stating that the license was governed by the laws of the State of Virginia. The Free Software Foundation argued that the choice-of-law clause was incompatible with the GNU GPL. BeOpen, CNRI, and the FSF negotiated a change to Python's free software license that would make it GPL-compatible. Python 1.6.1 is essentially the same as Python 1.6, with a few minor bug fixes, and with the new GPL-compatible license.

Version 2.0

Python 2.0 introduced list comprehensions, a feature borrowed from the functional programming languages SETL and Haskell. Python's syntax for this construct is very similar to Haskell's, apart from Haskell's preference for punctuation characters and Python's preference for alphabetic keywords. Python 2.0 also introduced a garbage collection system capable of collecting reference cycles.

Python 2.1 was close to Python 1.6.1, as well as Python 2.0. Its license was renamed Python Software Foundation License. All code, documentation and specifications added, from the time of Python 2.1's alpha release on, is owned by the Python Software Foundation (PSF), a non-profit organization formed in 2001, modeled after the Apache Software Foundation. The release included a change to the language specification to support nested scopes, like other statically scoped languages. (The feature was turned off by default, and not required, until Python 2.2.)

A major innovation in Python 2.2 was the unification of Python's types (types written in C), and classes (types written in Python) into one hierarchy. This single unification made Python's object model purely and consistently object oriented. Also added were generators which were inspired by Icon.

Java legacy

Python's standard library additions and syntactical choices were strongly influenced by Java in some cases: the logging package, introduced in version 2.3, the SAX parser, introduced in 2.0, and the decorator syntax that uses @, added in version 2.4

Future development

A Python Enhancement Proposal (or "PEP") is a standardized design document providing general information related to Python, including proposals, descriptions, and explanations for language features. PEPs are intended as the primary channel for proposing new features, and for documenting the underlying design rationale for all major elements of Python. Outstanding PEPs are reviewed and commented upon by van Rossum, the BDFL.

Python 3000

There are plans for a future version, to be called Python 3.0 (the project is called "Python 3000" or "Py3K") that will break backwards compatibility with the 2.x series in order to repair perceived flaws in the language. The guiding principle is to "reduce feature duplication by removing old ways of doing things".

Philosophy

Python 3.0 is being developed with the same philosophy as in prior versions, so any reference to Python philosophy will apply to Python 3.0 as well. However, as Python has accumulated new and redundant ways to program the same task, Python 3.0 has an emphasis on removing duplicative constructs and modules, in keeping with “There should be one—and preferably only one—obvious way to do it”.

Nonetheless, Python 3.0 will remain a multi-paradigm language. Coders will still have options among object orientation, structured programming, functional programming, and aspect-oriented programming and other paradigms, but within such broad choices, the details are intended to be more obvious in Python 3.0 than they have become in Python 2.x.

Timeline and compatibility

The first release candidate of Python 3.0 was released on September 17, 2008. The Python 2.x and Python 3.x series will coexist for several releases in parallel, where the 2.x series exists largely for compatibility and with some new features being backported from the 3.x series. PEP 3000 contains more information about the release schedule.

Python 3.0 will break backward compatibility. There is no requirement that Python 2.x code will run unmodified on Python 3.0. There are basic changes such as changing the print statement into a print function (so any use of print as a statement will cause the program to fail), and switching to Unicode for all text strings. Python's dynamic typing combined with the plans to change the semantics of certain methods of dictionaries, for example, makes perfect mechanical translation from Python 2.x to Python 3.0 very difficult. However, a tool called "2to3" does most of the job of translation, pointing out areas of uncertainty using comments or warnings. Even in an alpha stage, 2to3 appears to be fairly successful at performing the translation. PEP 3000 recommends keeping one source (for the 2.x series), and producing releases for the Python 3.x platform using 2to3. The resulting code should not be edited until the program no longer needs to run on Python 2.x.

Python 2.6 includes forward compatibility features, as well as a "warnings" mode that will warn of potential transition problems. Warnings will be reported for builtins which will no longer exist in 3.0, as well as various old Python 2.x features that Python 3.0 will remove (see PEP 361 for more information).

Features

Some of the major changes scheduled for Python 3.0 are:

  • Changing print so that it is a built-in function, not a statement. This makes it easier to change a module to use a different print function, as well as making the syntax more regular. In Python 2.6 this can be enabled by entering from __future__ import print_function.
  • Moving reduce (but not map or filter) out of the built-in namespace and into functools (the rationale being that operations using reduce are expressed more clearly using an accumulation loop);
  • Adding support for optional function annotations that can be used for informal type declarations or other purposes;
  • Unifying the str/unicode types, representing text, and introducing a separate immutable bytes type; and a mostly corresponding mutable bytearray type, which both represent arrays of bytes;
  • Removing backward-compatibility features, including old-style classes, integer-truncating division, string exceptions, and implicit relative imports.

Usage

Some of the largest projects that use Python are the Zope application server, YouTube, and the original BitTorrent client. Large organizations that make use of Python include Google, Yahoo!, CERN and NASA. ITA uses Python for some of its components.

Information Security

Python has also seen extensive use in the information security industry. Notably, in several of the tools offered by Immunity Security, in several of the tools offered by Core Security, in the Web application security scanner Wapiti, and in the fuzzer TAOF. Python is commonly used in exploit development.

Embedding

Python has been successfully embedded in a number of software products as a scripting language. It is commonly used in 3D animation packages, as in Houdini, Maya, Softimage XSI, TrueSpace, Poser, Modo, Nuke and Blender. It is also used in GIMP, Krita, Inkscape, Scribus and Paint Shop Pro. ESRI is now promoting Python as the best choice for writing scripts in ArcGIS. It is also used in games like Civilization IV and Mount&Blade as the control language for modding and event interaction. Eve Online, an MMORPG, is also built using Python.

Availability

For many operating systems, Python is a standard component; it ships with most Linux distributions, with NetBSD, and OpenBSD, and with Mac OS X. Red Hat Linux and Fedora both use the pythonic Anaconda installer. Gentoo Linux uses Python in its package management system, Portage, and the standard tool to access it, emerge. Pardus uses it for administration and during system boot.

Syntax and semantics

Python was intended to be a highly readable language. It aims toward an uncluttered visual layout, frequently using English keywords where other languages use punctuation. Python requires less boilerplate than traditional statically-typed structured languages such as C or Pascal, and has a smaller number of syntactic exceptions and special cases than either of these.

Indentation

Python uses whitespace indentation, rather than curly braces or keywords, to delimit statement blocks (a feature also known as the off-side rule). An increase in indentation comes after certain statements; a decrease in indentation signifies the end of the current block.

Statements and control flow

Python's statements include:

  • The if statement, which conditionally executes a block of code, along with else and elif (a contraction of else-if).
  • The for statement, which iterates over an iterable object, capturing each element to a local variable for use by the attached block.
  • The class statement, which executes a block of code and attaches its local namespace to a class, for use in object oriented programming.
  • The def statement, which defines a function.
  • The with statement which encloses a code block within a context manager (for example, acquiring a lock before the block of code is run, and releasing the lock afterwards).

Each statement has its own semantics: for example, the def statement does not execute its block immediately, unlike most other statements.

CPython does not support continuations, and according to Guido van Rossum it never will. However, better support for coroutine-like functionality is provided in 2.5, by extending Python's generators. Prior to 2.5, generators were lazy iterators — information was passed unidirectionally out of the generator. As of Python 2.5, it is possible to pass information back into a generator function.

Methods

Methods on objects are functions attached to the object's class; the syntax instance.method(argument) is, for normal methods and functions, syntactic sugar for Class.method(instance, argument). Python methods have an explicit self parameter to access instance data, in contrast to the implicit self in some other object-oriented programming languages (for example, Java, C++ or Ruby).

Typing

Python uses duck typing and has typed objects but untyped variable names. Type constraints are not checked at compile time; rather, operations on an object may fail, signifying that the given object is not of a suitable type. Despite not enforcing static typing, Python is strongly typed, forbidding nonsense operations (for example, adding a number to a string) rather than silently attempting to make sense of them.

Type Description Syntax Example
str, unicode An immutable sequence of characters 'Wikipedia', u'Wikipedia'
list Mutable, can contain mixed types [4.0, 'string', True]
tuple Immutable, can contain mixed types (4.0, 'string', True)
set, frozenset Unordered, contains no duplicates set([4.0, 'string', True])
frozenset([4.0, 'string', True])
dict Group of key and value pairs {'key1': 1.0, 'key2': False}
int A fixed precision number
(will be transparently expanded to an unlimited precision long, when it overflows the storage for an integer.)
42
2147483648L
float A real number 3.1415927
complex A complex number with real number and imaginary parts 3+2j
bool truth value True or False

Python also allows programmers to define their own types. This is done using classes, which are most often used for an object-oriented style of programming. New instances of classes are constructed by calling the class (eg, FooClass()), and the classes themselves are instances of the class type (itself an instance of itself), allowing metaprogramming and reflection.

Implementations

The mainstream Python implementation, also known as CPython, is written in C meeting the C89 standard,. CPython compiles the Python program into intermediate byte code, which is then executed by the virtual machine. It is distributed with a large standard library written in a mixture of C and Python. CPython ships in versions for many platforms, including Microsoft Windows and most modern Unix-like systems. CPython was intended from almost its very conception to be cross-platform; its use and development on esoteric platforms such as Amoeba, alongside more conventional ones like Unix, or Macintosh has greatly helped in this regard.

Cython is a code translator that is targeted at writing fast C extensions for the CPython interpreter. It accepts a language that is mostly Python (with a few exclusions) and has additional syntax support for most language features of the C language, and some features of the C++ language. The result of the translation process is optimised C code that is portable across many platforms and CPython versions. While not a stand-alone Python implementation by itself, Cython has the secret goal of becoming a real Python compiler as a companion to the CPython runtime.

Stackless Python is a significant fork of CPython that implements microthreads; it does not use the C memory stack. CPython uses a GIL to allow only one thread to execute at a time while the Stackless Python threads are independent of the OS and can run concurrently. Stackless Python is better suited to scalable tasks and for use on microcontrollers or other limited resource platforms due to the thread's light weight. It can be expected to run on approximately the same platforms that CPython runs on.

Jython compiles the Python program into Java byte code, which can then be executed by every Java Virtual Machine implementation. This also enables the utilization of Java class library functions from the Python program. IronPython follows a similar approach in order to run Python programs on the .NET Common Language Runtime.

PyPy is an experimental self-hosting implementation of Python, written in Python, that can output several types of bytecode, object code and intermediate languages.

Several programs exist to package the Python interpreter with application programs (or scripts) as standalone UNIX, Linux, Windows or Mac OS X executables, including Freeze, a pure Python utility that ships with Python, or py2exe, PyInstaller, cx_Freeze and py2app, all of which are available separately. Many third-party libraries for Python (and even some first-party ones) are only available on Windows, Linux, BSD, and Mac OS X.

In 2005 Nokia released a Python interpreter for the Series 60 mobile phones called PyS60. It includes many of the modules from the CPython implementations, but also some additional modules for integration with the Symbian operating system. This project has been kept up to date to run on all variants of the S60 platform and there are several third party modules available such as Miso and uitricks

ChinesePython (中蟒) is a Python programming language using Chinese language lexicon. Besides reserved words and variable names, most data type operations can be coded in Chinese as well.

Interpretational semantics

Most Python implementations (including CPython, the primary implementation) can function as a command line interpreter, for which the user enters statements sequentially and receives the results immediately. In short, Python acts as a shell. While the semantics of the other modes of execution (bytecode compilation, or compilation to native code) preserve the sequential semantics, they offer a speed boost at the cost of interactivity, so they are usually only used outside of a command-line interaction (eg, when importing a module).

Other shells add capabilities beyond those in the basic interpreter, including IDLE and IPython. While generally following the visual style of the Python shell, they implement features like auto-completion, retention of session state, and syntax highlighting.

Some implementations can compile not only to bytecode, but can turn Python code into machine code. So far, this has only been done for restricted subsets of Python. PyPy takes this approach, naming its restricted compilable version of Python RPython.

Psyco is a specialising just in time compiler which transforms bytecode to machine code at runtime. The produced code is specialised for certain data types and is faster than standard Python code. Psyco is compatible with all Python code, not only a subset.

Standard library

Python has a large standard library, commonly cited as one of Python's greatest strengths, providing pre-written tools suited to many tasks. This is deliberate and has been described as a "batteries included" Python philosophy. The modules of the standard library can be augmented with custom modules written in either C or Python. Recently, Boost C++ Libraries includes a library, Boost.Python, to enable interoperability between C++ and Python. Because of the wide variety of tools provided by the standard library, combined with the ability to use a lower-level language such as C and C++, which is already capable of interfacing between other libraries, Python can be a powerful glue language between languages and tools.

The standard library is particularly well tailored to writing Internet-facing applications, with a large number of standard formats and protocols (such as MIME and HTTP) already supported. Modules for creating graphical user interfaces, connecting to relational databases, arithmetic with arbitrary precision decimals, and manipulating regular expressions are also included. Python also includes a unit testing framework for creating test suites.

Some parts of the standard library are covered by specifications (for example, the WSGI implementation wsgiref follows PEP 333), but the majority of the modules are not. They are specified by their code, internal documentation, and test suite (if supplied). However, because most of the standard library is cross-platform Python code, there are only a few modules that must be altered or completely rewritten by alternative implementations.

Programming philosophy

Python is a multi-paradigm programming language. This means that, rather than forcing programmers to adopt a particular style of programming, it permits several styles: object oriented and structured programming are fully supported, and there are a number of language features which support functional programming and aspect-oriented programming. Many other paradigms are supported using extensions, such as pyDBC and Contracts for Python which allow Design by Contract. Python uses dynamic typing and a combination of reference counting and a cycle detecting garbage collector for memory management. An important feature of Python is dynamic name resolution, which binds method and variable names during program execution (also known as late binding).

Another target of the language's design is ease of extensibility, rather than having everything built into the language core. New built-in modules are easily written in C or C++. Python can also be used as an extension language for existing modules and applications that need a programmable interface. This design, of a small core language with a large standard library and an easily-extensible interpreter, was intended by van Rossum from the very start, due to his frustrations with ABC, which espoused the opposite mindset.

The design of Python offers limited support for functional programming in the Lisp tradition. However, there are significant parallels between the philosophy of Python and that of minimalist Lisp-family languages such as Scheme. The library has two modules (itertools and functools) that implement proven functional tools borrowed from Haskell and Standard ML.

While offering choice in coding methodology, the Python philosophy rejects exuberant syntax, such as in Perl, in favor of a sparser, less cluttered one. As with Perl, Python's developers expressly promote a particular "culture" or ideology based on what they want the language to be, favoring language forms they see as "beautiful", "explicit" and "simple". As Alex Martelli put it in his Python Cookbook (2nd ed., p.230): "To describe something as clever is NOT considered a compliment in the Python culture." Python's philosophy rejects the Perl "there is more than one way to do it" approach to language design in favor of "there should be one—and preferably only one—obvious way to do it".

Python eschews premature optimization, and moreover, rejects patches to non-critical parts of CPython which would offer a marginal increase in speed at the cost of clarity. It is sometimes described as 'slow'. However, most problems are not speed critical, and as computer hardware continues to become exponentially faster, languages do have more hardware resources available. When speed is a problem, Python programmers tend to try to optimize bottlenecks by algorithm improvements or data structure changes, by using a JIT compiler such as Psyco, or by rewriting the time-critical functions in "closer to the metal" languages such as C, or compile their Python code to fast C code using tools like Cython

Neologisms

A common neologism in the Python community is pythonic, which can have a wide range of meanings related to program style. To say that a piece of code is pythonic is to say that it uses Python idioms well, that it is natural or shows fluency in the language. Likewise, to say of an interface or language feature that it is pythonic is to say that it works well with Python idioms, that its use meshes well with the rest of the language.

In contrast, a mark of unpythonic code is that it attempts to "write C++ (or Lisp, or Perl) code in Python"—that is, provides a rough transcription rather than an idiomatic translation of forms from another language. The concept of pythonicity is tightly bound to Python's minimalist philosophy of readability and avoiding the "there's more than one way to do it" approach. Unreadable code or incomprehensible idioms are unpythonic.

Users and admirers of Python—most especially those considered knowledgeable or experienced—are often referred to as Pythonists, Pythonistas, and Pythoneers.

The prefix Py can be used to show that something is related to Python. Examples of the use of this prefix in names of Python applications or libraries include Pygame, a binding of SDL to Python (commonly used to create games); PyS60, an implementation for the Symbian Series 60 Operating System; PyQt and PyGTK, which bind Qt and GTK, respectively, to Python; and PyPy, a Python implementation written in Python. The prefix is also used outside of naming software packages: the major Python conference is named PyCon.

An important goal of the Python developers is making Python fun to use. This is reflected in the origin of the name (based on the television series Monty Python's Flying Circus), in the common practice of using Monty Python references in example code, and in an occasionally playful approach to tutorials and reference materials. For example, the metasyntactic variables often used in Python literature are spam and eggs, instead of the traditional foo and bar.

Influences on other languages

Python's design and philosophy have influenced several programming languages:

Python's development practices have also been emulated by other languages. The practice of requiring a document describing the rationale for, and issues surrounding, a change to the language (ie, a PEP) is also used in Tcl because of Python's influence.

References

See also

External links

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